ISPRS Hannover Workshop 2013, May 2013, Hannover, Germany

Transcription

1 New light-weight stereosopic spectrometric airborne imaging technology for highresolution environmental remote sensing Case studies in water quality mapping E. Honkavaara, T. Hakala, K. Nurminen, L. Markelin, Finnish Geodetic Institute J. Kirjasniemi, P. Ruokokoski, Lentokuva Vallas Oy A. Lindfors, Luode Consulting Oy J. Mäkynen, H. Saari, VTT Technical Research Center Finland ISPRS Hannover Workshop 2013, May 2013,

2 Content Introduction New imaging technology Empirical investigation Conclusions In co-operation Lentokuva Vallas Oy, Luode Consulting Oy, VTT Technical Research Center Finland Acknowledgement: This work was carried out partially in the MMEA research program coordinated by Cleen Ltd. with funding from the Finnish Funding Agency for Technology and Innovation, Tekes 2

3 Introduction New light-weight Fabry-Perot interferometer based camera technology Data cubes with adjustable spectral properties in a rectangular image format Weigh 600 g -> suitable for light Unmanned Airborne Vehicles (UAVs) and light Manned Airborne Vehicles (MAVs) Objective: To investigate and develop processing and use of the FPI imaging technology in environmental remote sensing and monitoring applications 3D-geometry, object reflectance signatures Measurement and processing in all weather conditions Previous studies in precision agriculture using 2011 and 2012 prototypes Case studies in water quality mapping Objective is to develop a fast and cost-efficient method to provide highresolution data from complex environments where the use of traditional sampling methods is limited, such as lakes, rivers and harbour areas Assessment of its potential in measurement and monitoring of various water quality parameters algae blooms, turbidity, organic carbon, total phosphorus concentrations, chlorophyll-a 3

4 FPI spectral camera technology 4

5 Fabry-Perot interferometer based tuneable spectral imager, 2012 prototype Hyperspectral imagtery in frame format Spectral data cube by changing the width of Fabry-Perot air gap Developed by VTT Technical Research Finland (Heikki Saari et al.) Custom optics, CMOS detector Image size: 1024 x 648 pixels (2xbinned), Pixel 11 μm C=10.9 mm, F-number < 3.0 Application based filter selection between nm , , , , nm Spectral resolution FWHM ISPRS Hannover Workshop 2013, May 2013, 5

6 UAV operation UAV Autopilot IMU GPS Payload Spectral imager High spatial resolution imager GPS Irradiance sensors Ground control station Mission design and control Insitu reference measurements: irradiance, reflectance targets, In typical flight data cubes with spectral layers Georeferencing data Irradiance data Insitu data 6

7 Processing of FPI image data Desired output product: water leaving spectral reflectance mosaic Rigorous FPI spectral data cube generation is the basis for further processes Geometric processing with standard photogrammetric methods: orientations, DSMs New processing is developed for radiometric model calculation using frame format images Output products: DSMs, spectrometric point clouds, spectrometric image mosaics, spectrometric stereoscopic data 7

8 Spectral data cube generation Sensor radiometric and spectral calibration in laboratory using an integrating sphere and monochromator by VTT Spectral smile correction Layer matching Geometric transformation by using feature based matching to a single reference layer. Affine or projective transformation. Strategy: Reference layer for each spctral region, apriori matching in layer order Quality statistics: Standard deviations of model parameters, magnitudes of parameters, residuals G R NIR 8

9 A new method for reflectance image generation of frame images Data Overlapping spectral rectangle format data cubes Tasks Eliminate radiometric disturbances caused by sensor instability and illumination/atmosphere BRDF compensation Reflectance calibration Approach Radiometric model parameters using radiometric block adustment with a network of radiometric tie points Optional insitu irradiance measurements Reflectance images using reflectance targets Light source Observer C G E i r D A A B φ r North F φ i O 9

10 Output products: DSMs, hyperspectral image mosaics, hyperspectral point clouds Honkavaara, E., et. al. 2012b. Hyperspectral reflectance signatures and point clouds for precision agriculture by light weight UAV imaging system, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, , doi: /isprsannals-i , 2012.

11 Empirical study Campaigns by UAV and MAV in lake Petäjärvi, Sjökulla Shallow lake in agricultural area extending to an area of 2 km by 2 km 11

12 UAV Campaign, Helicopter UAV, 4 kg payload, autopilot Flight parameters according to legistlation in Finland: Flying height 150 m, GSD 15 cm; Visual control by UAV pilot FPI spectral camera Filters nm, nm and nm, spectral bands Continuos interval mode Weather: Fluctuating cloudiness Insitu data Water quality measurements by Luode Consulting Oy Spectral reference targets and measurements by FGI 12

13 MAV campaign using single-engine aircraft, By Lentokuva Vallas Oy, OH-CNU, Cessna 172 Reims Rocket Block Height: 440 m over terrain, Speed 39 m/s, 2 km x 4 km = 8 km 2, 10 lines, 200 m Flight line spacing Overlaps: 63%, 56% GSD: 45 cm 14 ms integration times, image interval > 2 s, Filter nm, 20 channels Weather: Cloudy, rainy Insitu data Sjökulla test site Ground reflectance reference data with Avantes hand held spectrometer Water quality measurements by Luode Oy Lentokuva Vallas Oy Lentokuva Vallas Oy 13

14 UAV and MAV campaigns at Sjökulla/Petäjärvi in 2012 UAV MAV 14

15 Results 15

16 Signal-to-noise ratio MAV 45 cm UAV 15 cm 16

17 Georeferencing using national open topographic orthophotos and DSMs Geoferencing a block with a threelayer image, apply the orientations to layer matched datacubes Ground reference: national open digital surface models and orthophotos, 90 GCPs GPS data was not used Orientations for images containing only water by interpolation from adjacent images Bae Systems Socet Set environment Automatic tie point measurement Self-calibrating bundle block adjustment σ0 = 0.5 m RMS at GCPs: X: 0.69 m, Y: 0.70 m, Z: 0.18 m The National Land Survey of Finland, Colour orthophoto and Elevation model 10 m, 01/2013, ata_licence_version1_

18 Reflectance images of MAV data Extensive illumination variations during the flight, water shower in the middle of the flight Radiometric block adjustment Relative shift correction or relative multiplicative correction Reflectance transformation using empirical line method Coefficient of variation in radiometric tie points without corrections, with corrections Potential shadowing by the floor hole was uncorrected No correction Relative multiplicative 18

19 Vegetation and soil reflectance Insitu measurement: rain seriously descreased reflectance The FPI reflectance followed quite well the reference reflectance 19

20 Lake reflectance from MAV data G R NIR Reflectance in 3 m x 3 m image window Reflectance is influenced by algae blooms and turbidity Further analysis is in operation 20

21 MAV and UAV spectral data cube mosaics MAV mosaic, GSD 1 m , rainy weather UAV mosaic, GSD 20 cm , sunny weather 21

22 Conclusions Preliminary results of the FPI spectral camera in water quality mapping application were presented. Data analysis will continue. Very promising technology High spatial resolution, stereoscopic, spectrometric image data FPI camera is operational from dynamic, light-weight, UAV platforms, suitable also for operation from MAV platform Can be operated in difficult illumination conditions: under clouds, rain Well suited for time-critical and monitoring applications, such as water quality, agriculture, mining environments, disasters UAV operation best suited for areas <25 ha (depending on legistlation), MAV for larger areas Radiometric processing technology for images collected in diverse weather conditions is needed, radiometric aspects need to be carefully considered Ongoing studies at FGI Improving radiometric and geometric processing Procedure for SI-traceable UAV reflectance measurements Analysis in forests, agriculture, water environment, mining environment 22

23 Thank you! 23